Field of the Invention
The solution according to the present invention relates to analysis of traffic flows of moving physical entities. In detail, the solution according to the present invention relates to management of empirical data collected for performing traffic analysis.
Overview of the Related Art
Traffic analysis is aimed at identifying and predicting variations in the flow (e.g., vehicular traffic flow) of physical entities (e.g., land vehicles) moving in a geographic area of interest (e.g., a urban area) and over a predetermined observation period (e.g., a 24 hours observation period).
A typical, but not limitative, example of traffic analysis is represented by the analysis of vehicular (cars, trucks, etc.) traffic flow over the routes of a geographic area of interest. Such analysis allows achieving a more efficient planning of the transportation infrastructure within the area of interest and also it allows predicting how changes in the transportation infrastructure, such as for example closure of roads, changes in a sequencing of traffic lights, construction of new roads and new buildings, can impact on the vehicular traffic.
In the following for traffic analysis it is intended the analysis of the movements of physical entities through a geographic area. Such physical entities can be vehicles (e.g., cars, trucks, motorcycles, public transportation buses) and/or individuals.
Since it is based on statistical calculations, traffic analysis needs a large amount of empirical data to be collected in respect of the area of interest and the selected observation period, in order to provide accurate results. In order to perform the analysis of traffic, the collected empirical data are then usually arranged in a plurality of matrices, known in the art as Origin-Destination (O-D) matrices. The O-D matrices are based upon a partitioning of both the area of interest and the observation period.
For partitioning the area of interest, the area is subdivided into a plurality of zones, each zone being defined according to several parameters such as for example, authorities in charge of the administration of the zones (e.g., a municipality), typology of land lots in the area of interest (such as open space, residential, agricultural, commercial or industrial lots) and physical barriers (e.g., rivers) that can hinder traffic (physical barriers can be used as zone boundaries). The size of the zones in which the area of interest can be subdivided, and consequently the number of zones, is proportional to the level of detail requested for the traffic analysis (i.e., city districts level, city level, regional level, state level, etc.).
As well, the observation period can be subdivided into one or more time slots, each time slot being defined according to known traffic trends, such as for example peak traffic hours corresponding to when most commuters travel to their workplace and/or travel back to home. The length of the time slots (and thus their number) is proportional to the level of detail requested for the traffic analysis over the considered observation period.
Each entry of a generic O-D matrix comprises the number of physical entities moving from a first zone (origin) to a second zone (destination) of the area of interest. Each O-D matrix corresponds to one time slot out of the one or more time slots in which the considered observation period can be subdivided. In order to obtain a reliable traffic analysis, sets of O-D matrices should be computed over a plurality of analogous observation periods and should be combined so as to obtain O-D matrices with a higher statistical value. For example, empirical data regarding the movements of physical entities should be collected over a number of consecutive days (each corresponding to a different observation period), and for each day a corresponding set of O-D matrices should be computed.
A typical method for collecting empirical data used to compute O-D matrices related to a specific area of interest is based on submitting questionnaires to, or performing interviews with inhabitants of the area of interest and/or to inhabitants of the neighboring areas about their habits in relation to their movements, and/or by installing vehicle count stations along routes of the area of interest for counting the number of vehicles moving along such routes. The Applicant has observed that this method has very high costs and it requires a long time for collecting a sufficient amount of empirical data. Due to this, O-D matrices used to perform traffic analysis are built seldom, possibly every several years, and become out-of-date.
In the art, several alternative solutions have been proposed for collecting empirical data used to compute O-D matrices.
For example, U.S. Pat. No. 5,402,117 discloses a method for collecting mobility data in which, via a cellular radio communication system, measured values are transmitted from vehicles to a computer. The measured values are chosen so that they can be used to determine O-D matrices without infringing upon the privacy of the users.
In Chinese Patent Application No. 102013159 a number plate identification data-based area dynamic origin and destination (OD) data acquiring method is described. The dynamic OD data is the dynamic origin and destination data, wherein O represents origin and D represents destination. The method comprises the steps of: dividing OD areas according to requirements, wherein the minimum time unit is 5 minutes; uniformly processing data of each intersection in the area every 15 minutes by a traffic control center; detecting number plate data; packing the number plate identification data; uploading the number plate identification data to the traffic control center; comparing a plate number with an identity (ID) number passing through the intersections; acquiring the time of each vehicle passing through each intersection; acquiring the number of each intersection in the path through which each vehicle passes from the O point to the D point by taking the plate number as a clue; sequencing the intersections according to time sequence and according to the number of the vehicles which pass through between the nodes calculating a dynamic OD data matrix.
WO 2007/031370 relates to a method for automatically acquiring traffic inquiry data, e.g. in the form of an O-D matrix, especially as input information for traffic control systems. The traffic inquiry data are collected by means of radio devices placed along the available routes.
Nowadays, mobile phones have reached a thorough diffusion among the population of many countries, and mobile phone owners almost always carry their mobile phone with them. Since mobile phones communicates with a plurality of base stations of the mobile phone networks, and each base station operates over a predetermined geographic area (or cell) which is known to the mobile phone network, mobile phones result to be optimal candidates as tracking devices for collecting data useful for performing traffic analysis. For example, N. Caceres, J. Wideberg, and F. Benitez “Deriving origin destination data from a mobile phone network”, Intelligent Transport Systems, IET, vol. 1, no. 1, pp. 15-26, 2007, describes a mobility analysis simulation of moving vehicles along a highway covered by a plurality of GSM network cells. In the simulation the entries of O-D matrices are determined by identifying the GSM cells used by the mobile phones in the moving vehicles for establishing voice calls or sending sms.
US 2006/0293046 proposes a method for exploiting data from a wireless telephony network to support traffic analysis. Data related to wireless network users are extracted from the wireless network to determine the location of a mobile station. Additional location records for the mobile station can be used to characterize the movement of the mobile station: its speed, its route, its point of origin and destination, and its primary and secondary transportation analysis zones. Aggregating data associated with multiple mobile stations allows characterizing and predicting traffic parameters, including traffic speeds and volumes along routes.
In F. Calabrese et al. “Estimating Origin-Destination Flows Using Mobile Phone Location Data”, IEEE Pervasive, pp. 36-44, October-December 2011 (vol. 10 no. 4), a further method is proposed that envisages to analyze position variations of mobile devices in a respective mobile communication network in order to determine entries of O-D matrices.